Advertisement

Jet Substructure: Concepts and Tools

  • Simone Marzani
  • Gregory Soyez
  • Michael Spannowsky
Chapter
  • 339 Downloads
Part of the Lecture Notes in Physics book series (LNP, volume 958)

Abstract

In this chapter we genuinely start to discuss jet substructure per se. We begin with a generic description of the physics concepts which are at the foundation of jet substructure techniques. We then discuss ideas on how to assess the performance of these tools, including their sheer discriminating power as well as their resilience against non-perturbative effects. The remaining part of the chapter is dedicated to a description of jet substructure techniques themselves, covering an extensive list of tools which we have organised, as much as possible, according to the basic concepts discussed at the beginning of the chapter.

References

  1. 4.
    J. Butterworth, B. Cox, J.R. Forshaw, WW scattering at the CERN LHC. Phys. Rev. D65, 096014 (2002). [hep-ph/0201098]Google Scholar
  2. 5.
    J.M. Butterworth, A.R. Davison, M. Rubin, G.P. Salam, Jet substructure as a new Higgs search channel at the LHC. Phys. Rev. Lett. 100, 242001 (2008). [0802.2470]Google Scholar
  3. 8.
    A. Altheimer, A. Arce, L. Asquith, J. Backus Mayes, E. Bergeaas Kuutmann et al., Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012. Eur. Phys. J. C74, 2792 (2014). [1311.2708]Google Scholar
  4. 47.
    H.-M. Chang, M. Procura, J. Thaler, W.J. Waalewijn, Calculating track-based observables for the LHC. Phys. Rev. Lett. 111, 102002 (2013) . [1303.6637]Google Scholar
  5. 48.
    H.-M. Chang, M. Procura, J. Thaler, W.J. Waalewijn, Calculating track thrust with track functions. Phys. Rev. D88, 034030 (2013) . [1306.6630]Google Scholar
  6. 50.
    A.J. Larkoski, S. Marzani, G. Soyez, J. Thaler, Soft drop. J. High Energy Phys. 1405, 146 (2014). [1402.2657]Google Scholar
  7. 64.
    M. Cacciari, G.P. Salam, G. Soyez, The catchment area of jets. J. High Energy Phys. 04, 005 (2008) . [0802.1188]Google Scholar
  8. 65.
    F.A. Dreyer, G.P. Salam, G. Soyez, The Lund jet plane. J. High Energy Phys. 12, 064 (2018). [1807.04758]Google Scholar
  9. 120.
    M. Dasgupta, A. Powling, L. Schunk, G. Soyez, Improved jet substructure methods: Y-splitter and variants with grooming. J. High Energy Phys. 12, 079 (2016). [1609.07149]Google Scholar
  10. 121.
    G.P. Salam, L. Schunk, G. Soyez, Dichroic subjettiness ratios to distinguish colour flows in boosted boson tagging. J. High Energy Phys. 03, 022 (2017). [1612.03917]Google Scholar
  11. 122.
    J.R. Andersen et al., Les Houches 2017: physics at TeV colliders Standard Model Working Group Report, in 10th Les Houches Workshop on Physics at TeV Colliders (PhysTeV 2017), Les Houches, June 5–23, 2017 (2018). 1803.07977ADSGoogle Scholar
  12. 123.
    M. Dasgupta, A. Fregoso, S. Marzani, G.P. Salam, Towards an understanding of jet substructure. J. High Energy Phys. 1309, 029 (2013). [1307.0007]Google Scholar
  13. 124.
    F.A. Dreyer, L. Necib, G. Soyez, J. Thaler, Recursive soft drop. J. High Energy Phys. 06, 093 (2018). [1804.03657]Google Scholar
  14. 125.
    D. Krohn, J. Thaler, L.-T. Wang, Jet trimming. J. High Energy Phys. 1002, 084 (2010) . [0912.1342]Google Scholar
  15. 126.
    S.D. Ellis, C.K. Vermilion, J.R. Walsh, Techniques for improved heavy particle searches with jet substructure. Phys.Rev. D80, 051501 (2009). [0903.5081]Google Scholar
  16. 127.
    D.E. Kaplan, K. Rehermann, M.D. Schwartz, B. Tweedie, Top tagging: a method for identifying boosted hadronically decaying top quarks. Phys. Rev. Lett. 101, 142001 (2008). [0806.0848]Google Scholar
  17. 128.
    CMS collaboration, A Cambridge-Aachen (C-A) based Jet Algorithm for boosted top-jet tagging (2009). CMS-PAS-JME-09-001Google Scholar
  18. 129.
    CMS collaboration, Boosted Top Jet Tagging at CMS, CMS-PAS-JME-13-007.Google Scholar
  19. 130.
    M. Dasgupta, M. Guzzi, J. Rawling, G. Soyez, Top tagging : an analytical perspective. J. High Energy Phys. 09, 170 (2018). [1807.04767]Google Scholar
  20. 131.
    A.J. Larkoski, J. Thaler, W.J. Waalewijn, Gaining (mutual) information about Quark/Gluon discrimination. J. High Energy Phys. 11, 129 (2014). [1408.3122]Google Scholar
  21. 132.
    C.F. Berger, T. Kucs, G.F. Sterman, Event shape/energy flow correlations. Phys. Rev. D68, 014012 (2003). [hep-ph/0303051]Google Scholar
  22. 133.
    L.G. Almeida, S.J. Lee, G. Perez, G.F. Sterman, I. Sung, J. Virzi, Substructure of high-p T jets at the LHC. Phys. Rev. D79, 074017 (2009). [0807.0234]Google Scholar
  23. 134.
    J. Gallicchio, M.D. Schwartz, Quark and Gluon Tagging at the LHC. Phys. Rev. Lett. 107, 172001 (2011). [1106.3076]Google Scholar
  24. 136.
    J.R. Andersen et al., Les Houches 2015: Physics at TeV Colliders Standard Model Working Group Report, in 9th Les Houches Workshop on Physics at TeV Colliders (PhysTeV 2015), Les Houches, June 1–19, 2015 (2016). 1605.04692ADSGoogle Scholar
  25. 137.
    F. Pandolfi, D. Del Re, Search for the standard model Higgs boson in the H → ZZ → llqq decay channel at CMS. PhD thesis, Zurich, ETH, 2012Google Scholar
  26. 138.
    CMS collaboration, S. Chatrchyan et al., Search for a Higgs boson in the decay channel \(H \to ZZ^{(*)} \to q \bar q \, l^- l^+ \) in pp collisions at \(\sqrt {s}=7\) TeV. J. High Energy Phys. 04, 036 (2012). [1202.1416]Google Scholar
  27. 139.
    B.T. Elder, J. Thaler, Aspects of track-assisted mass (2018). 1805.11109Google Scholar
  28. 140.
    J. Thaler, K. Van Tilburg, Identifying boosted objects with N-subjettiness. J. High Energy Phys. 03, 015 (2011). [1011.2268]Google Scholar
  29. 141.
    I.W. Stewart, F.J. Tackmann, W.J. Waalewijn, N-jettiness: an inclusive event shape to veto jets. Phys. Rev. Lett. 105, 092002 (2010). [1004.2489]Google Scholar
  30. 142.
    J. Thaler, K. Van Tilburg, Maximizing boosted top identification by minimizing N-subjettiness. J. High Energy Phys. 02, 093 (2012). [1108.2701]Google Scholar
  31. 143.
    A.J. Larkoski, G.P. Salam, J. Thaler, Energy correlation functions for jet substructure. J. High Energy Phys. 1306, 108 (2013). [1305.0007]Google Scholar
  32. 144.
    I. Moult, L. Necib, J. Thaler, New Angles on energy correlation functions. J. High Energy Phys. 12, 153 (2016). [1609.07483]Google Scholar
  33. 145.
    A.J. Larkoski, I. Moult, D. Neill, Building a better boosted top tagger. Phys. Rev. D91, 034035 (2015). [1411.0665]Google Scholar
  34. 146.
    P.T. Komiske, E.M. Metodiev, J. Thaler, Energy flow polynomials: a complete linear basis for jet substructure. J. High Energy Phys. 04, 013 (2018). [1712.07124]Google Scholar
  35. 147.
    P.T. Komiske, E.M. Metodiev, J. Thaler, Energy flow networks: deep sets for particle jets. J. High Energy Phys. 01, 121 (2019). [1810.05165]Google Scholar
  36. 148.
    C. Frye, A.J. Larkoski, J. Thaler, K. Zhou, Casimir meets Poisson: improved quark/gluon discrimination with counting observables. J. High Energy Phys. 09, 083 (2017). [1704.06266]Google Scholar
  37. 149.
    J. Thaler, L.-T. Wang, Strategies to identify boosted tops. J. High Energy Phys. 07, 092 (2008). [0806.0023]Google Scholar
  38. 150.
    G. Soyez, G.P. Salam, J. Kim, S. Dutta, M. Cacciari, Pileup subtraction for jet shapes. Phys. Rev. Lett. 110, 162001 (2013). [1211.2811]Google Scholar
  39. 151.
    M. Field, G. Gur-Ari, D. A. Kosower, L. Mannelli and G. Perez, Three-Prong Distribution of Massive Narrow QCD Jets, Phys. Rev. D87 (2013) 094013, [1212.2106].Google Scholar
  40. 152.
    S.D. Ellis, A. Hornig, T.S. Roy, D. Krohn, M.D. Schwartz, Qjets: a non-deterministic approach to tree-based jet substructure. Phys. Rev. Lett. 108, 182003 (2012). [1201.1914]Google Scholar
  41. 153.
    S.D. Ellis, A. Hornig, D. Krohn, T.S. Roy, On statistical aspects of Qjets. J. High Energy Phys. 01, 022 (2015). [1409.6785]Google Scholar
  42. 154.
    CMS collaboration, A.M. Sirunyan et al., Search for low-mass resonances decaying into bottom quark-antiquark pairs in proton-proton collisions at \(\sqrt {s} =\) 13 TeV. Phys. Rev. (2018). [1810.11822]Google Scholar
  43. 155.
    J. Dolen, P. Harris, S. Marzani, S. Rappoccio, N. Tran, Thinking outside the ROCs: designing decorrelated taggers (DDT) for jet substructure. J. High Energy Phys. 05, 156 (2016). [1603.00027]Google Scholar
  44. 156.
    I. Moult, B. Nachman and D. Neill, Convolved Substructure: Analytically Decorrelating Jet Substructure Observables, J. High Energy Phys. 05, 002 (2018). [1710.06859]Google Scholar
  45. 157.
    D. Napoletano, G. Soyez, Computing N-subjettiness for boosted jets. J. High Energy Phys. 12, 031 (2018). [1809.04602]Google Scholar
  46. 158.
    M. Dasgupta, A. Powling, A. Siodmok, On jet substructure methods for signal jets. J. High Energy Phys. 08, 079 (2015). [1503.01088]Google Scholar
  47. 159.
    K. Kondo, Dynamical likelihood method for reconstruction of events with missing momentum. 1: method and toy models. J. Phys. Soc. Jpn. 57, 4126–4140 (1988)Google Scholar
  48. 160.
    D0 collaboration, V.M. Abazov et al., A precision measurement of the mass of the top quark. Nature 429, 638–642 (2004). [hep-ex/0406031]Google Scholar
  49. 161.
    P. Artoisenet, P. de Aquino, F. Maltoni, O. Mattelaer, Unravelling \(t\overline {t}h\) via the matrix element method. Phys. Rev. Lett. 111, 091802 (2013), [1304.6414]Google Scholar
  50. 163.
    J.R. Andersen, C. Englert, M. Spannowsky, Extracting precise Higgs couplings by using the matrix element method. Phys. Rev. D87, 015019 (2013). [1211.3011]Google Scholar
  51. 164.
    D.E. Soper, M. Spannowsky, Finding physics signals with shower deconstruction. Phys. Rev. D84, 074002 (2011) [1102.3480]Google Scholar
  52. 165.
    D.E. Soper, M. Spannowsky, Finding top quarks with shower deconstruction. Phys. Rev. D87, 054012 (2013). [1211.3140]Google Scholar
  53. 166.
    ATLAS collaboration, Performance of shower deconstruction in ATLAS. ATLAS-CONF-2014-003Google Scholar
  54. 167.
    T. Plehn, G.P. Salam, M. Spannowsky, Fat jets for a light Higgs. Phys. Rev. Lett. 104, 111801 (2010). [0910.5472]Google Scholar
  55. 168.
    T. Plehn, M. Spannowsky, M. Takeuchi, D. Zerwas, Stop reconstruction with tagged tops. J. High Energy Phys. 10, 078 (2010). [1006.2833]Google Scholar
  56. 169.
    G. Kasieczka, T. Plehn, T. Schell, T. Strebler, G.P. Salam, Resonance searches with an updated top tagger. J. High Energy Phys. 06, 203 (2015). [1503.05921]Google Scholar
  57. 170.
  58. 171.
  59. 172.
  60. 173.
    M. Cacciari, G.P. Salam, G. Soyez, SoftKiller, a particle-level pileup removal method. Eur. Phys. J. C75, 59 (2015). [1407.0408]Google Scholar
  61. 174.
    D. Bertolini, P. Harris, M. Low, N. Tran, Pileup per particle identification. J. High Energy Phys. 10, 059 (2014). [1407.6013]Google Scholar
  62. 175.
    M. Cacciari, G.P. Salam, Pileup subtraction using jet areas. Phys. Lett. B659, 119–126 (2008). [0707.1378]Google Scholar
  63. 176.
    SM MC Working Group, SM and NLO MULTILEG Working Group collaboration, J. Alcaraz Maestre et al., The SM and NLO multileg and SM MC working groups: summary report, in Proceedings, 7th Les Houches Workshop on Physics at TeV Colliders, Les Houches, May 30–June 17, 2011 (2012), pp. 1–220. 1203.6803Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Simone Marzani
    • 1
  • Gregory Soyez
    • 2
  • Michael Spannowsky
    • 3
  1. 1.Dipartimento di FisicaUniversità di GenovaGenovaItaly
  2. 2.Institut de Physique TheoriqueCNRS UMR 3681, CEA SaclayGif-sur-Yvette cedexFrance
  3. 3.Department of Physics, Institute for Particle Physics PhenomenologyDurham UniversityDurhamUK

Personalised recommendations